DyNetViewer: a Cytoscape app for dynamic network construction, analysis and visualization
Bibliographic record
Abstract
Summary: The molecular interactions in a cell are varying with time and surrounded environmental cues. The construction and analysis of dynamic molecular networks can elucidate dynamic cellular mechanisms of different biological functions and provide a chance to understand complex diseases at the systems level. Here, we develop DyNetViewer, a Cytoscape application that provides a range of functionalities for the construction, analysis and visualization of dynamic protein-protein interaction networks. The current version of DyNetViewer consists of four different dynamic network construction methods, twelve topological variation analysis methods and four clustering algorithms. Moreover, visualization of different topological variation of nodes and clusters over time enables users to quickly identify the most variations across many network states. Availability and implementation: DyNetViewer is freely available with tutorials at the Cytoscape (3.4+) App Store (http://apps.cytoscape.org/apps/dynetviewer). Contact: limin@mail.csu.edu.cn. Supplementary information: Supplementary data are available at Bioinformatics online.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.122 | 0.036 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".